Case Study · BFSI — Microfinance

Five disconnected systems, one governed lakehouse: modernizing data for 3.1M microfinance borrowers

How a regional microfinance institution replaced spreadsheet reconciliation and siloed core-banking data with a governed Snowflake lakehouse, real-time streaming, and a single trusted borrower record.

Regulatory reporting cycle cut from 21 days to 3 — with a single governed view of every borrower for the first time.

Executive Summary

The client, a regional microfinance institution serving more than 3 million borrowers, was running loan origination, collections, and credit-risk scoring across five disconnected core-banking and loan-management systems accumulated through three acquisitions. Regulatory reporting required manual spreadsheet reconciliation and consumed roughly three weeks of analyst time every cycle. aiDataWorks modernized the estate onto a governed Snowflake lakehouse with real-time ingestion and master data management, giving risk and compliance teams a single trusted borrower view for the first time and cutting the regulatory reporting cycle to three days.

The Challenge

Five core-banking and loan-origination systems never fully reconciled after three acquisitions → the same borrower could appear as up to four different customer records, corrupting credit-risk scores.
Nightly batch ETL jobs failed silently on a regular basis → risk and finance teams frequently worked from data that was 24–48 hours stale without any way to know it.
Central-bank regulatory reporting relied on manual spreadsheet reconciliation across five systems → each reporting cycle consumed roughly three weeks of analyst time and carried high error risk.
No formal data lineage or ownership model existed → a single "monthly disbursement" metric produced three different numbers depending on which team was asked.
Field agents on the mobile loan-origination app had no visibility into real-time credit exposure → branches occasionally extended new credit to already-delinquent borrowers.
Legacy on-premise infrastructure had no elastic scaling → month-end close routinely pushed the batch window past 14 hours, delaying the next business day's branch operations.

Target-State Architecture

Diagram showing five source systems (core banking, loan origination, mobile agent app, collections, branch POS) flowing through an ingestion and streaming layer (Informatica IDMC, Apache Kafka) into a Bronze, Silver, Gold Snowflake lakehouse modeled with dbt, governed by Informatica MDM and a data catalog, and served to regulatory reporting, a real-time risk API, Power BI, and the mobile agent app. — Target-state data architecture: source systems → ingestion & streaming → governed Snowflake lakehouse → consumption layer. Download diagram ↓

Solution & Approach

Ingestion & Streaming

Deployed Informatica IDMC change-data-capture connectors against all five core-banking and loan-management databases for near-real-time capture.
Introduced an Apache Kafka event backbone to decouple source systems from downstream consumers, replacing brittle nightly batch jobs.
Added schema-drift detection so upstream field changes surface as alerts instead of silent pipeline failures.

Transformation & Modelling

Rebuilt the warehouse as a Bronze/Silver/Gold Snowflake lakehouse, modeled with dbt for version-controlled, testable transformations.
Standardized a single borrower_360 entity resolved across all five source systems.
Rewrote the regulatory-reporting logic as a governed dbt model instead of ad hoc spreadsheets.

Data Quality & Governance

Stood up Informatica MDM to match, merge, and survive borrower records across systems, closing the duplicate-identity gap.
Published lineage from source field to regulatory report inside a central data catalog, with a named data owner per domain.
Added automated data-quality SLAs — completeness, freshness, referential integrity — with alerting on breach.

Consumption Layer

Delivered a real-time credit-exposure view to branch and field agents through a lightweight risk API.
Rebuilt regulatory and executive reporting in Power BI on top of governed Gold-layer models.
Handed the platform over under an ongoing DMaaS managed-services model for monitoring, tuning, and enhancement requests.

At a glance

Industry BFSI — Microfinance
Region South & Southeast Asia
Company size 2,400+ employees · 3.1M active borrowers · 460 branches
Duration 9 months, discovery to stabilization
Services Cloud Data Platform Modernization, Master Data Management, Data Governance, Managed DMaaS

Snowflake Informatica IDMC Informatica MDM Apache Kafka dbt Power BI AWS

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Outcomes

Three metrics moved immediately after go-live — and kept holding under real production load.

3 days

Regulatory reporting cycle

Before: 21 days → After: 3 days

92%

Fewer duplicate borrower profiles

Before: 14% duplicate rate → After: 1.1% duplicate rate

2.5 hrs

Month-end close batch window

Before: 14 hours → After: 2.5 hours

Risk teams now see a single, governed view of every borrower across all five legacy systems, instead of reconciling exports by hand.
Field agents get real-time credit-exposure data on the same mobile app they already use for loan origination, cutting over-extension incidents.
Central-bank reporting moved from a spreadsheet-driven fire-drill to a scheduled, auditable pipeline the compliance team trusts.

“The reporting cycle used to eat three weeks out of every month. Now it's a scheduled job we barely think about, and for the first time our risk team trusts the number on the screen.”

— VP of Risk & Compliance, the client

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